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* specific goal: known all pathways that could be altered between the 2 (or more) conditions that we are testing. We aim in this analysis to have a global and comprehensive view of what is happening in the cells, a snapshots of the entire cell at the moment the RNA was extracted. | * specific goal: known all pathways that could be altered between the 2 (or more) conditions that we are testing. We aim in this analysis to have a global and comprehensive view of what is happening in the cells, a snapshot of the entire cell at the moment the RNA was extracted. |
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* The steps are to download the data from GEO and process the data. Then the first step of the enrichment analysis will be to run GSEA (Gene Set Enrichment Analysis, which is a tool from the Broad Institute). To run GSEA, we need to create a file called a rank file from the array data and use it with a pathway database downloadable from the Baderlab website. Then we will create a network called an enrichment map using the Cytoscape and the application called EnrichmentMap; for that we will need to create an expression file and use also the GSEA results that we have just run. | * The steps are to download the data from GEO and process the data. Then the first step of the enrichment analysis will be to run GSEA (Gene Set Enrichment Analysis, which is a tool from the Broad Institute): to do it, we need to create a file called a rank file from the array data and use it with a pathway database downloadable from the Baderlab website. Then we will create a network called an enrichment map using the Cytoscape software; for that we will need to create an expression file and use also the GSEA results that we have just run. |
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* /!\ the rank files contains all the genes: do not filter by only genes differentially expressed. We are ranking the genes from the top up regulated to the top down regulated. The genes that do not vary and not of interest are in the middle of the list and GSEA will look for significance at the 2 extremity of the lists only. | |
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* This file contains all known and curated biological functions. For each of these functions, the names of genes known to be implicated in this function are listed beside this function. The gene set enrichment analysis will look if the top differentially expressed genes are included in some of these pathways. It will also assess if this enrichment can happen by chance only or not. | * The pathway database file contains all known and curated biological functions that we are going to test in this pathway enrichment analysis. For each of these functions, the names of genes known to be implicated in this function are listed beside this function. The gene set enrichment analysis will look if the top differentially expressed genes are included in some of these pathways. It will also assess if this enrichment can happen by chance only or not. |
A to Z protocol to create an EnrichmentMap from Gene Expression Data and using GSEA (Gene Set Enrichment Analysis)
- system requirement to run this workflow:
- we need to have 2GB of free/system memory to run GSEA or navigate through an Enrichment Map. Thus having at least 4GB of system memory is recommended.
- goal (what is the goal of enrichment analysis)
- specific goal: known all pathways that could be altered between the 2 (or more) conditions that we are testing. We aim in this analysis to have a global and comprehensive view of what is happening in the cells, a snapshot of the entire cell at the moment the RNA was extracted.
- description of the steps: this section briefly explains the steps that will be followed to run the gene set enrichment workflow as well as the files needed for it.
- The steps are to download the data from GEO and process the data. Then the first step of the enrichment analysis will be to run GSEA (Gene Set Enrichment Analysis, which is a tool from the Broad Institute): to do it, we need to create a file called a rank file from the array data and use it with a pathway database downloadable from the Baderlab website. Then we will create a network called an enrichment map using the Cytoscape software; for that we will need to create an expression file and use also the GSEA results that we have just run.
- How to create a rank file (.rnk)
- the rank file contains only 2 columns: the gene identifiers (official gene symbol in this workflow) as the first column and the differential expression values for each gene as the second column. In this protocol, we will use the t value from a moderated Student's t-test. Headers (column names) should be removed. The format should be tab delimited (meaning that the columns are separated by tabs) and the file extension should be .rnk.
the rank file is a format described in the GSEA documentation: http://www.broadinstitute.org/cancer/software/gsea/wiki/index.php/Data_formats
- the rank file will be used to run the gene set enrichment analysis (GSEA).
the rank files contains all the genes: do not filter by only genes differentially expressed. We are ranking the genes from the top up regulated to the top down regulated. The genes that do not vary and not of interest are in the middle of the list and GSEA will look for significance at the 2 extremity of the lists only.
- How to get the pathway database file (.gmt)
- The pathway database file contains all known and curated biological functions that we are going to test in this pathway enrichment analysis. For each of these functions, the names of genes known to be implicated in this function are listed beside this function. The gene set enrichment analysis will look if the top differentially expressed genes are included in some of these pathways. It will also assess if this enrichment can happen by chance only or not.
- In this protocol we are going to use a file that include pathways from different sources (e.g Gene Ontology, Reactome, Kegg,...). We observed that having the most comprehensive set of pathways gave more sensitive results. Although databases that are included in this file can be overlapping, they are not 100% identical -thus using several ones give more sensitivity - and the clusters created by the enrichment map from these different sources add confidence about the perturbation of a given biological function.
The link to the database file compiled by the BaderLab and updated monthly can be found at: http://download.baderlab.org/EM_Genesets/ (look for the current release at the bottom of the list) and a description of how the file is being created at: http://baderlab.org/GeneSets
- How to run GSEA
GSEA can be downloaded from http://www.broadinstitute.org/gsea/index.jsp
- you need to enter a valid e-mail address before going to the download section
- How to create an expression file
- Altough only 2 files were needed to run GSEA, a additional file, called the expression needs to be prepared to create an enrichmentmap.
- How to create a map
- What is the next step, how to use the map
- How to create a figure
- How to interpret the results
- What next
- (How to preprocess the data using R)
- (How to preprocess the data using Excel)
FIRST EXAMPLE WITH AFFYMETRIX MICROARRAY DATA
- description of the data
Download the data from GEO
Installation
1) install R (http://www.r-project.org/)
2) install RStudio (http://www.rstudio.com/)
3) Go through on online R tutorial (e.g. this one: http://www.cyclismo.org/tutorial/R/)
How to preprocess the data (normalization, QC, differential expression)
How to update the annotations
How to create a rank file
How to create an expression file
How to run GSEA
How to create a map